Description
Building a Simple Data Analyst AI Agent with Llama and Flask. This hands-on course teaches you how to build a simple data analyst AI agent using the open-source Llama language model and the Flask framework. Without the need for expensive APIs or advanced programming knowledge, participants will learn how to run the Llama model locally and create a lightweight application with Flask. The application will be able to answer user questions based on data stored in a Postgres database, providing functionality similar to a simple retrieval-based production system. The course begins by teaching the principles of prompt engineering and covers basic techniques such as in-text learning, thought chains, and thought trees. Next, learners will set up a Flask server, connect it to a Postgres database, and create an endpoint that receives and processes user queries and returns AI-generated answers from the database data. This course provides a direct and practical path to applying large language models to small, real-world projects, and builds a solid foundation in prompt engineering and AI application development by teaching you how to build a working prototype.
What you will learn
- Understand and apply core prompt engineering techniques such as In-Context Learning (ICL), Chain of Thought (CoT), and Tree of Thought (ToT).
- Set up and run an open source Large Language Model (LLM) (Llama) locally without the need for paid APIs.
- Build a simple AI-powered Flask application that connects to a Postgres SQL database.
- Design prompts that allow the AI agent to understand the user’s questions and retrieve accurate answers from structured data.
- Develop a basic understanding of connecting natural language processing to SQL databases through APIs.
This course is suitable for people who:
- Beginners curious about artificial intelligence (AI), prompt engineering, and lightweight AI applications.
- Data analysts who want to explore AI-enhanced workflows.
- Developers interested in experimenting with Recovery-Based Manufacturing (RAG) principles.
- Data engineers.
- Anyone who wants a practical, quick, and clear introduction to using Large Language Models (LLMs) in real-world mini-projects.
- Beginner to intermediate learners curious about Prompt Engineering, LLMs, and AI Agents.
- Data analysts and Python developers who want to enhance their skills with AI tools.
- Tech enthusiasts who want to build a real-world project combining AI, databases, and web APIs.
- Anyone interested in building their first simple application in the Recovery-Based Generation (RAG-style) style.
Course details
- Publisher: Udemy
- Instructor: Kiril Spiridonov
- Training level: Beginner to advanced
- Training duration: 2 hours and 47 minutes
- Number of lessons: 23
Course topics
Prerequisites for the Building a Simple Data Analyst AI Agent with Llama and Flask course
- Basic understanding of what a database is.
- Python and SQL experience are helpful but not required — all key concepts are explained clearly during the course.
- Access to a computer where you can install Python packages and run Docker containers.
Course images
Sample course video
Installation Guide
After Extract, view with your favorite player.
Subtitles: None
Quality: 720p
Download link
Downloadly
Rapidgator link
File(s) password: www.downloadly.ir
File size
1.2 GB
